As Generative AI matures, we’re shifting from one-off prompt completion to orchestrating intelligent, multi-step workflows. In this space, two open-source tools from Microsoft AutoGen and Microsoft Semantic Kernel (SK) are leading the way. While both help developers build AI-first applications, they take different approaches.
- AutoGen is like assembling a virtual room of AI experts(agents) discussing a task, each contributing uniquely
- Semantic Kernel is like having a smart assistant who remembers, plans, and takes precise action using your tools and data
| Feature | AutoGen (by Microsoft) | Semantic Kernel (SK) (by Microsoft) |
| Primary Purpose | Multi-agent orchestration for complex AI workflows | Composable AI apps using functions (skills), memory, and planners |
| Core Use Case | Coordinating multiple LLM agents in a system | Integrating AI into apps with memory, planning, and semantic functions |
| Level of Abstraction | Higher (agent behaviour orchestration) | Lower to medium (function composition and planning) |
| Developer Paradigm | Agentic programming model (chat-based agents) | Functional composition model (skills, planners, memory) |
| LLM-centric | Yes, strongly LLM-centric | Yes, but integrates traditional programming logic well |
| Program language | Python Only | Python, C#, Java (preview) |
| Memory support | Agent-to-agent behavior | Built-in semantic memory |
| Integration | LLM-focused agents (Azure Open AI) | Native + LLM + API-based integration (Azure search, Open AI etc.) |
Here’s a quick, crisp breakdown to help you decide which fits your use case be
Choose AutoGen when:
- You want agents to talk to each other to solve problems
- You’re simulating decision-making between roles (e.g., researcher, planner, coder)
- You’re building autonomous agents or Chain-of-Thought systems
Choose Semantic Kernel when:
- You’re building a personal assistant or business copilot
- You need memory, planning, and integration with enterprise systems
- You want AI to call native functions and APIs intelligently
| Use Case | Best Tool | Notes |
|---|---|---|
| Multi-agent conversations (e.g., assistant + researcher + coder) | AutoGen | Built specifically for this |
| Task planning and execution with memory and function chaining | Semantic Kernel | More suitable with its Planner APIs |
| LLM agent that queries a database and formats data | Semantic Kernel | Easier with native and semantic skills |
| Simulating agent collaboration (e.g., debate, decision making) | AutoGen | Strong agent framework |
| Adding LLM features to existing enterprise apps | Semantic Kernel | Better SDK integration and hosting options |
What Is AutoGen?
AutoGen is a multi-agent orchestration framework. It lets you simulate intelligent conversations between LLM-powered agents each with its own role, memory, tools, and behaviors.
Think of it like programming a team of expert AI personas that collaborate to solve tasks: Ideal for agent-based systems, multi-role AI workflows, and autonomous decision making.
What Is Semantic Kernel?
Semantic Kernel (SK) is a lightweight SDK that brings LLMs into your applications through composable skills, planners, and memory. It blends AI with traditional programming, letting you build task-driven copilots and assistants: Ideal for LLM-powered apps that need planning, function chaining, and long-term memory.
